Merge branch 'microsoft:main' into main

.github/workflows/generator-generic-ossf-slsa3-publish.yml

@@ -0,0 +1,66 @@
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+# This workflow lets you generate SLSA provenance file for your project.
+# The generation satisfies level 3 for the provenance requirements - see https://slsa.dev/spec/v0.1/requirements
+# The project is an initiative of the OpenSSF (openssf.org) and is developed at
+# https://github.com/slsa-framework/slsa-github-generator.
+# The provenance file can be verified using https://github.com/slsa-framework/slsa-verifier.
+# For more information about SLSA and how it improves the supply-chain, visit slsa.dev.
+
+name: SLSA generic generator
+on:
+  workflow_dispatch:
+  release:
+    types: [created]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    outputs:
+      digests: ${{ steps.hash.outputs.digests }}
+
+    steps:
+      - uses: actions/checkout@v3
+
+      # ========================================================
+      #
+      # Step 1: Build your artifacts.
+      #
+      # ========================================================
+      - name: Build artifacts
+        run: |
+            # These are some amazing artifacts.
+            echo "artifact1" > artifact1
+            echo "artifact2" > artifact2
+
+      # ========================================================
+      #
+      # Step 2: Add a step to generate the provenance subjects
+      #         as shown below. Update the sha256 sum arguments
+      #         to include all binaries that you generate
+      #         provenance for.
+      #
+      # ========================================================
+      - name: Generate subject for provenance
+        id: hash
+        run: |
+          set -euo pipefail
+
+          # List the artifacts the provenance will refer to.
+          files=$(ls artifact*)
+          # Generate the subjects (base64 encoded).
+          echo "hashes=$(sha256sum $files | base64 -w0)" >> "${GITHUB_OUTPUT}"
+
+  provenance:
+    needs: [build]
+    permissions:
+      actions: read   # To read the workflow path.
+      id-token: write # To sign the provenance.
+      contents: write # To add assets to a release.
+    uses: slsa-framework/slsa-github-generator/.github/workflows/generator_generic_slsa3.yml@v1.4.0
+    with:
+      base64-subjects: "${{ needs.build.outputs.digests }}"
+      upload-assets: true # Optional: Upload to a new release

1-Introduction/1-intro-to-ML/translations/README.fr.md

@@ -37,7 +37,7 @@ Le cerveau d'un enfant et ses sens perçoivent l'environnement qui les entourent
 
 Le [cerveau humain](https://www.livescience.com/29365-human-brain.html) perçoit des choses du monde réel, assimile les informations perçues, fait des décisions rationnelles et entreprend certaines actions selon le contexte. C'est ce que l'on appelle se comporter intelligemment. Lorsque nous programmons une reproduction du processus de ce comportement à une machine, c'est ce que l'on appelle intelligence artificielle (IA).
 
-Bien que le terme peut être confu, machine learning (ML) est un important sous-ensemble de l'intelligence artificielle. **ML se réfère à l'utilisation d'algorithmes spécialisés afin de découvrir des informations utiles et de trouver des schémas non observés depuis des données perçues pour corroborer un processus de décision rationnel**.
+Bien que le terme puisse être confus, le machine learning (ML) est un important sous-ensemble de l'intelligence artificielle. **Le ML consiste à utiliser des algorithmes spécialisés afin de découvrir des informations utiles et de trouver des schémas non observés depuis des données perçues pour corroborer un processus de décision rationnel**.
 
 ![AI, ML, deep learning, data science](../images/ai-ml-ds.png)
 
@@ -66,19 +66,19 @@ Dans ce cours, vous allez apprendre :
 - Neural networks
 - IA
 
-Afin d'avoir la meilleur expérience d'apprentissage, nous éviterons les complexités des réseaux neuronaux, du 'deep learning' (construire un modèle utilisant plusieurs couches de réseaux neuronaux) et IA, dont nous parlerons dans un cours différent. Nous offirons aussi un cours à venir sur la data science pour concentrer sur cet aspect de champs très large.
+Afin d'avoir la meilleure expérience d'apprentissage, nous éviterons les complexités des réseaux neuronaux, du 'deep learning' (construire un modèle utilisant plusieurs couches de réseaux neuronaux) et IA, dont nous parlerons dans un cours différent. Nous offirons aussi un cours à venir sur la data science pour nous concentrer sur cet aspect de champs très large.
 
-## Pourquoi etudier le machine learning ?
+## Pourquoi étudier le machine learning ?
 
 Le machine learning, depuis une perspective systémique, est défini comme la création de systèmes automatiques pouvant apprendre des schémas non observés depuis des données afin d'aider à prendre des décisions intelligentes.
 
 Ce but est faiblement inspiré de la manière dont le cerveau humain apprend certaines choses depuis les données qu'il perçoit du monde extérieur.
 
-✅ Penser une minute aux raisons qu'une entreprise aurait d'essayer d'utiliser des stratégies de machine learning au lieu de créer des règles codés en dur.
+✅ Pensez une minute aux raisons qu'une entreprise aurait d'essayer d'utiliser des stratégies de machine learning au lieu de créer des règles codés en dur.
 
 ### Les applications du machine learning
 
-Les applications du machine learning sont maintenant pratiquement partout, et sont aussi omniprésentes que les données qui circulent autour de notre société (générés par nos smartphones, appareils connectés ou autres systèmes). En prenant en considération l'immense potentiel des algorithmes dernier cri de machine learning, les chercheurs ont pu exploités leurs capacités afin de résoudre des problèmes multidimensionnels et interdisciplinaires de la vie avec d'important retours positifs 
+Les applications du machine learning sont maintenant pratiquement partout, et sont aussi omniprésentes que les données qui circulent autour de notre société (générées par nos smartphones, appareils connectés ou autres systèmes). En prenant en considération l'immense potentiel des algorithmes dernier cri de machine learning, les chercheurs ont pu exploiter leurs capacités afin de résoudre des problèmes multidimensionnels et interdisciplinaires de la vie avec d'important retours positifs.
 
 **Vous pouvez utiliser le machine learning de plusieurs manières** :
 

5-Clustering/1-Visualize/README.md

@@ -258,7 +258,7 @@ Note, when the top genre is described as 'Missing', that means that Spotify did
 1. Do a quick test to see if the data correlates in any particularly strong way:
 
     ```python
-    corrmat = df.corr()
+    corrmat = df.corr(numeric_only=True)
     f, ax = plt.subplots(figsize=(12, 9))
     sns.heatmap(corrmat, vmax=.8, square=True)
     ```
@@ -300,7 +300,7 @@ Are these three genres significantly different in the perception of their dancea
 1. Create a scatter plot:
 
     ```python
-    sns.FacetGrid(df, hue="artist_top_genre", size=5) \
+    sns.FacetGrid(df, hue="artist_top_genre", height=5) \
        .map(plt.scatter, "popularity", "danceability") \
        .add_legend()
     ```

5-Clustering/2-K-Means/README.md

@@ -169,7 +169,7 @@ Previously, you surmised that, because you have targeted 3 song genres, you shou
 
     ```python
     plt.figure(figsize=(10,5))
-    sns.lineplot(range(1, 11), wcss,marker='o',color='red')
+    sns.lineplot(x=range(1, 11), y=wcss, marker='o', color='red')
     plt.title('Elbow')
     plt.xlabel('Number of clusters')
     plt.ylabel('WCSS')

README.md

@@ -12,7 +12,7 @@
 
 > 🌍 Travel around the world as we explore Machine Learning by means of world cultures 🌍
 
-Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about **Machine Learning**. In this curriculum, you will learn about what is sometimes called **classic machine learning**, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum. Pair these lessons with our ['Data Science for Beginners' curriculum](https://aka.ms/datascience-beginners), as well!
+Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about **Machine Learning**. In this curriculum, you will learn about what is sometimes called **classic machine learning**, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our [AI for Beginners' curriculum](https://aka.ms/ai4beginners). Pair these lessons with our ['Data Science for Beginners' curriculum](https://aka.ms/ds4beginners), as well!
 
 Travel with us around the world as we apply these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment, and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.
 
@@ -24,24 +24,10 @@ Travel with us around the world as we apply these classic techniques to data fro
 
 **🤩 Extra gratitude to Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, and Vidushi Gupta for our R lessons!**
 
----
-
-## Announcement - New Curriculum on Generative AI was just released!
-
-We just released a 12 lesson curriculum on generative AI. Come learn things like:
-
-- prompting and prompt engineering
-- text and image app generation
-- search apps
-
-As usual, there's a lesson, assignments to complete, knowledge checks and challenges.
-
-Check it out:
-
-> https://aka.ms/genai-beginners
-
 # Getting Started
 
+> [find all additional resources for this course in our Microsoft Learn collection](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+
 **[Students](https://aka.ms/student-page)**, to use this curriculum, fork the entire repo to your own GitHub account and complete the exercises on your own or with a group:
 
 - Start with a pre-lecture quiz.
@@ -84,7 +70,7 @@ By ensuring that the content aligns with projects, the process is made more enga
 
 > Find our [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), and [Translation](TRANSLATIONS.md) guidelines. We welcome your constructive feedback!
 
-## Each lesson includes:
+## Each lesson includes
 
 - optional sketchnote
 - optional supplemental video
@@ -132,6 +118,8 @@ By ensuring that the content aligns with projects, the process is made more enga
 |  Postscript   |            Real-World ML scenarios and applications            |      [ML in the Wild](9-Real-World/README.md)       | Interesting and revealing real-world applications of classical ML                                                               |                                             [Lesson](9-Real-World/1-Applications/README.md)                                              |                         Team                         |
 |  Postscript   |            Model Debugging in ML using RAI dashboard          |      [ML in the Wild](9-Real-World/README.md)       | Model Debugging in Machine Learning using Responsible AI dashboard components                                                              |                                             [Lesson](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
 
+> [find all additional resources for this course in our Microsoft Learn collection](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
+
 ## Offline access
 
 You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, and then in the root folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`.
@@ -140,7 +128,7 @@ You can run this documentation offline by using [Docsify](https://docsify.js.org
 
 Find a pdf of the curriculum with links [here](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
 
-## Help Wanted!
+## Help Wanted
 
 Would you like to contribute a translation? Please read our [translation guidelines](TRANSLATIONS.md) and add a templated issue to manage the workload [here](https://github.com/microsoft/ML-For-Beginners/issues).
 
@@ -148,12 +136,12 @@ Would you like to contribute a translation? Please read our [translation guideli
 
 Our team produces other curricula! Check out:
 
-- [AI for Beginners](https://aka.ms/ai-beginners)
+- [AI for Beginners](https://aka.ms/ai4beginners)
 - [Data Science for Beginners](https://aka.ms/datascience-beginners)
-- [Generative AI for Beginners](https://aka.ms/genai-beginners)
+- [**New Version 2.0** - Generative AI for Beginners](https://aka.ms/genai-beginners)
 - [**NEW** Cybersecurity for Beginners](https://github.com/microsoft/Security-101??WT.mc_id=academic-96948-sayoung)
 - [Web Dev for Beginners](https://aka.ms/webdev-beginners)
 - [IoT for Beginners](https://aka.ms/iot-beginners)
-- [Machine Learning for Beginners](https://aka.ms/ml-beginners)
+- [Machine Learning for Beginners](https://aka.ms/ml4beginners)
 - [XR Development for Beginners](https://aka.ms/xr-dev-for-beginners)
 - [Mastering GitHub Copilot for AI Paired Programming](https://aka.ms/GitHubCopilotAI)

index.html

@@ -4,10 +4,10 @@
 <head>
   <meta charset="UTF-8">
   <title>Machine Learning for Beginners</title>
-  <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1" />
-  <meta name="description" content="Description">
+  <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">
+  <meta name="description" content="Introduction to Machine Learning for Beginners">
   <meta name="viewport"
-    content="width=device-width, user-scalable=no, initial-scale=1.0, maximum-scale=1.0, minimum-scale=1.0">
+    content="width=device-width, initial-scale=1.0, maximum-scale=1.0, minimum-scale=1.0, user-scalable=no">
   <link rel="icon" type="image/png" href="images/favicon.png">
   <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/docsify-themeable@0/dist/css/theme-simple.css">
 </head>
@@ -22,7 +22,7 @@
       auto2top: true,
     }
   </script>
-  <script src="//cdn.jsdelivr.net/npm/docsify/lib/docsify.min.js"></script>
+  <script src="https://cdn.jsdelivr.net/npm/docsify@4/lib/docsify.min.js"></script>
 </body>
 
 </html>

quiz-app/package.json

@@ -14,9 +14,9 @@
     "vue-router": "^3.5.3"
   },
   "devDependencies": {
-    "@vue/cli-plugin-babel": "~4.5.0",
-    "@vue/cli-plugin-eslint": "~4.5.0",
-    "@vue/cli-service": "~4.5.0",
+    "@vue/cli-plugin-babel": "~5.0.8",
+    "@vue/cli-plugin-eslint": "~5.0.8",
+    "@vue/cli-service": "~5.0.8",
     "babel-eslint": "^10.1.0",
     "eslint": "^6.7.2",
     "eslint-plugin-vue": "^6.2.2",